This application will use secondary data analysis to examine acute post surgical pain management practice in an academic medical center using large datasets created from electronic health records (EHR) and medication administration systems. The primary aim of the study is to analyze pain management by both hypothesis tests &data mining. Current pain management practice is informed with standards disseminated by the American Pain Society reflecting the best evidence-based knowledge of how to treat pain, yet implementation remains slow. Pain remains an important post operative issue, resulting in a level of patient anxiety that may surpass fear of a unsuccessful surgical outcome. One of the challenges in improving pain management is the surveillance of nurse behaviors (i.e., assessment, intervention, and re-assessment) through documentation audits. Examination of electronic nursing documentation may present opportunities to discover knowledge and ways to improve practice. In this study, adherence to pain treatment guidelines will be measured in the electronic medical record, primarily via nursing documentation of patient care, including medication administration and nursing flow sheets. Vanderbilt University Medical Center, as a pioneer in implementation of clinical application information systems, e.g. bar code medication administration charting and nursing documentation flow sheet charting, can measure the implementation levels of evidence based standards in large populations of all recipients of care regardless of age, gender or race. We plan to use the data analysis to estimate the prevalence, severity, treatment, and effectiveness of therapies to manage acute pain in hospitalized patients. Pain relief will be the primary outcome. We will analyze the temporal association of pain assessment, intervention, and reassessment to severity of pain scores in relation to scheduled (versus as needed) medications, multimodal medications, and behavioral interventions. The study will use a regression analysis to examine the outcome of implemented pain standards on the level of pain experienced by the patient during hospitalization. Pain scores (on the standard scale of 0-10) will be predicted by time to reassessment, number of reassessments, opioid administration, non-opioid analgesic administration, non-pharmacological therapies, and the number of therapies administered together (intensity of interventions). In addition, data mining will be performed to extract implicit, previously unknown, and useful information for enhancing pain management. The long term goal of this study is to determine feasibility of using this electronic data for ongoing surveillance that could be monitored to improve performance using a possible incremental intervention similar the nurse led acute pain service extension of rapid response teams implemented by some health care institutions. PUBLIC HEALTH RELEVANCE: Project Narrative: Despite the emergence of guidelines and standard for management of acute post operative pain, unrelieved pain persists, driving increased costs of hospitalization. This work will perform a secondary data analysis of electronic health records, including nursing pain assessments and therapies (both pharmacologic and non-pharmacologic). We will also data mine for other implicit, previously unknown, useful information such as conditions that should trigger or escalate an alert for an acute pain management intervention.